Column gives results from a reduced form specification regressing market prices and controls on worker productivity directly, and column provides the results of my preferred two-stage least squares specification instrumenting for wages with market prices. When I instrument for wages, their effect on worker productivity remains statistically insignificant, but the relevant point estimate becomes barely positive. The temperature response function is quite stable across columns and lending support to the conclusion that I accurately recover a true relationship. While the richness of my data allows me to exploit intra-day variation in temperature, I can also collapse my data to the day-level and investigate how daily temperature affects daily worker productivity. Figure 1.15 reports the results of three different day-level temperature specifications. The first uses time-weighted average daily temperature experienced by each picker, the second uses daily maximum temperature, and the third uses daily minimum temperature. Overall, the results from these specifications support the qualitative results of my primary specification: extreme temperatures lower picker productivity, and cool temperatures are more damaging than very hot temperatures. One threat to the credibility of my findings in tables 1.2 and 1.3 is that temperature and wages may affect workers’ labor supply, both on the intensive and extensive margins. That is, workers may decide to work fewer hours on a particularly hot day, or choose not to come to work at all if the piece rate wage is particularly low.Such behavior would bias my estimates of how temperature and wages affect productivity by introducing unobserved systematic selection into or out of my sample. I investigate this possibility in table 1.5 by regressing temperature, wages, and controls on both hours worked and the probability of working.

In column ,cannabis grower supplies the dependent variable is the number of hours worked by a picker in a single day, and temperature is measured as a time-weighted average experienced by the picker during that day. Here, I control for a picker’s start-time rather than their picking “midpoint.” In column , the dependent variable is an indicator for whether a picker worked at all in a given day, and temperature is measured as a daily midpoint temperature: /2. I use daily midpoint temperature in column in order to provide a consistent comparison between employees who show up to work and employees who do not, since I do not know when or for how long these absent employees would have worked had they come to work. Figure 1.16 displays the relevant temperature results from columns and of table 1.5. Overall, table 1.5 reports that neither wages nor temperatures affect labor supply in a statistically significant way. Similar to Graff Zivin and Neidell , I find the labor supply of agricultural workers to be highly inelastic in the short run. This also matches the findings of Sudarshan et al. for weaving workers in India. This evidence gives me confidence in the validity of my baseline results.I now turn to how temperature affects berry pickers’ wage responsiveness. Table 1.6 reports the results of estimating a variant of equation separately across eight temperature bins.I find that wages have no meaningful effect on productivity at most temperatures, but have a statistically significant and positive effect on productivity at cool temperatures: those between 50 and 60 degrees. In particular, my estimate suggests an increase in the piece rate wage of one cent per pound at temperatures below 60 degrees increases average productivity by 0.28 pounds per hour. This reflects an elasticity of productivity with respect to the wage of roughly 1.6 at cool temperatures,and an elasticity statistically indistinguishable from zero at other temperatures. This “productivity elasticity” is considerably smaller than the 2.14 number estimated by Paarsch and Shearer . Table 1.7, which repeats the analysis from table 1.6 using ordinary least squares , highlights the importance of instrumenting for piece rate wages. This table highlights two important things. First, the effects of wages on productivity at low temperatures do not show up in a statistically significant way without correctly instrumenting for wages with market prices. Second, I am able to rule out any dramatically large effect of wages on productivity at most temperatures.

Another threat to my findings is that workers who do not out-earn the hourly minimum wage in a given day may shirk when they know that additional productivity will not increase their take-home pay. Figure 1.13 reports the frequency with which workers fall below this minimum wage threshold. I face an econometric problem if the effects of temperature reduce workers’ productivity, increase the probability that workers earn the minimum wage, and hence encourage shirking. To ensure my findings are not meaningfully altered by this phenomenon, I re-estimate my main results using only picker observations where the picker out-earns the minimum wage for the day. This procedure drops my number of picking period observations from 305,980 to 257,689: a decrease of 15.8%. Figure 1.17 and table 1.8 present the results of my main temperature and piece rate wage specifications using this subsample. My findings remain qualitatively stable and statistically significant.Finally, even if temperature and wages do not affect labor supply directly in a statistically significant manner, and even though worker-specific fixed effects capture individual workers’ average productivity levels, I still face a potential adverse selection problem. Specifically, if variation in temperature and wages affects which sorts of workers choose to show up for work, my results may capture workforce compositional effects rather than individual productivity effects. To address this concern, I re-estimate my results only using observations from those workers who work more than thirty days in the relevant season. The intention here is to focus on workers who are likely to have the least elastic extensive labor supply. The results of this robustness exercise are presented in figure 1.18 and table 1.9. Taken together with the other available evidence, these results largely support my baseline findings. My primary finding is that labor productivity, on average, is very inelastic with respect to piece rate wages: I can reject with 95% confidence even modest positive elasticities of up to 0.7. This upper bound is considerably lower than the estimates derived by Paarsch and Shearer and Haley . I show that, without controlling for seasonality, a regression of productivity on piece rate wages results in a negative and significant point estimate . However, even once I control for seasonality, a naïve OLS regression of productivity on piece rate wage may be biased toward zero of table 1.4.

By instrumenting for piece rate wages with the market price for blueberries, I can identify a precisely-estimated inelastic effect of table 1.2. However, my primary specification makes the restrictive assumption that wages affect productivity linearly and in the same manner at all temperatures. Table 1.6 confirms that piece rates’ effect on productivity is very much non-linear across different temperatures. Specifically, wages seem to spur productivity at cool temperatures . At other temperatures, wages do not affect productivity in a statistically significant way. This empirical finding directly challenges one of the core assumptions of the model presented in section 1.2.1: that productivity always rises with the wage . What is going on? One possible explanation for my findings is that, at moderate to hot temperatures, workers’ face some binding physiological constraint on effort that prevents them from responding to changes in their wage. Put bluntly, blueberry pickers in general may already be “giving all they’ve got” at the temperatures and wages I observe.Figure 1.19 summarizes this possibility using the theoretical framework developed in section 1.2.1. While the model in section 1.2.1 is straightforward and tractable, it is not the only way to conceptualize worker effort and productivity. In particular, rather than modeling effort as an unrestricted choice variable,dry racks for weed one could assume each worker has a finite daily budget of effort that must be allocated across different activities throughout a day and Becker. Such a model would allow Xr to be zero or even negative under certain conditions, implying a backwards-bending effort supply curve, somewhat analogous to the canonical backward-bending labor supply curve . The downside of such models is that they fail to provide comparative statics that can be tested with the data I observe in this setting. A growing literature has rigorously documented the non-linear impact of temperature on everything from corn yields to cognitive performance , but has not focused specifically on how temperature affects agricultural workers.Nevertheless, several recent papers in this literature seem particularly relevant to my findings. Adhvaryu et al. show that factory workers in India produce more output when heat-emitting conventional light bulbs are replaced LED lighting, especially on hot days. Sudarshan et al. find similar evidence that temperature reduces worker productivity in a variety of Indian manufacturing firms. Finally, Seppänen et al. show that temperature even has large effects on the productivity of office workers.Other researchers have asked broader questions about how temperature affects aggregate production or labor decisions at the county- or country-level. The growing consensus is that weather shocks – particularly exposures to extreme heat – reduce aggregate production in a wide variety of settings. For instance, Hsiang exploits natural variation in cyclones to find negative impacts of high temperatures in both agricultural and non-agricultural sectors at the country-level. Deryugina and Hsiang and Park find similar county level effects of daily temperature in the United States, despite widespread adoption of air conditioning. Heal and Park document relevant findings throughout the economics literature and provide a useful theoretical link between heat’s physiological effects and aggregate economic activity.Extreme heat may reduce aggregate production through several channels. The first possibility, discussed at length in the previous paragraph, is that employees are less productive while working at high temperatures.

Another possibility is that employees may choose to work fewer hours when temperatures are particularly high. In other words, there may be a labor supply response to temperature on the extensive margin. Graff Zivin and Neidell provide support for this hypothesis by analyzing data from the American Time Use Survey. They find that at high temperatures, individuals reduce the time they spend working and increase the time they spend on indoor leisure. Finally, temperature can affect even broader aspects of the labor market like aggregate demand for agricultural labor in India , or the composition of labor in urban vs. rural regions of Eastern Africa . While this paper examines how a particularly salient environmental condition, temperature, affects labor productivity, previous research has shown that other environmental factors matter as well. Chang et al. , for instance, find that outdoor air pollution negatively affects the indoor productivity of pear packers. The same authors conduct a similar exercise using data from Chinese call-centers and find comparable results. Adhvaryu et al. find a steep pollution-productivity gradient in the context of an Indian garment factory, and Graff Zivin and Neidell find large damages from ozone in an agricultural context somewhat similar to my own. In an older case study, Crocker and Horst, Jr. study seventeen citrus pickers in southern California and find negative effects of both high temperatures and air pollution. It is useful to think of temperature not as a single sufficient statistic to describe environmental quality, but rather as one condition among many that is relevant for understanding labor productivity. This paper makes several important contributions to the literature discussed above. First, because I observe berry-pickers’ productivity multiple times during a single day, the variation I observe in both productivity and temperature is much more temporally precise than in many previous studies. Additionally, since I use temperature observations that are taken hourly, and sometimes more frequently, I do not need to interpolate temperature over time. Second, I study a setting where both very hot and cool temperatures have negative effects on productivity, highlighting the particularities of different production processes when it comes to temperature impacts. Third, and most importantly, I look at how how environmental conditions and incentive schemes interact.Table 1.2 and figure 1.14 provide my estimates of the direct effects of temperature on labor productivity in the California blueberry industry. Whereas most previous studies have focused on the negative effects of extreme heat , I find that cool temperatures have just as large negative effects as very hot temperatures, if not larger.